Variable Importance for every Local school districts
Broward County, FL (FT)
df_ft = df_fit_district %>%
filter(sitename == "Broward County, FL (FT)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ft,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 100 1 1000
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.4925654 -0.056737581 0.17950185 0.34368644
## 2 1 1.149757e-03 0.4925654 -0.056737581 0.17950185 0.34368644
## 3 1 1.321941e-03 0.4925654 -0.051160153 0.17950185 0.33656961
## 4 1 1.519911e-03 0.4984477 -0.049544696 0.16862674 0.33446827
## 5 1 1.747528e-03 0.4984477 -0.049544696 0.16862674 0.33446827
## 6 1 2.009233e-03 0.5046977 -0.042205970 0.17734522 0.34743140
## 7 1 2.310130e-03 0.5164624 -0.026257737 0.17656366 0.34203839
## 8 1 2.656088e-03 0.5171569 -0.031176005 0.19175430 0.36128249
## 9 1 3.053856e-03 0.5227124 -0.032333214 0.18657151 0.35567429
## 10 1 3.511192e-03 0.5223856 -0.037707081 0.18591781 0.35239706
## 11 1 4.037017e-03 0.5216912 -0.058512931 0.17421325 0.33077623
## 12 1 4.641589e-03 0.5216912 -0.058512931 0.17421325 0.33077623
## 13 1 5.336699e-03 0.5220180 -0.064884544 0.17733092 0.33050426
## 14 1 6.135907e-03 0.5279003 -0.058390486 0.18071486 0.33342625
## 15 1 7.054802e-03 0.5396650 -0.051472700 0.16712265 0.32768303
## 16 1 8.111308e-03 0.5455474 -0.045313280 0.15832318 0.31923948
## 17 1 9.326033e-03 0.5514297 -0.039152596 0.15630865 0.31598590
## 18 1 1.072267e-02 0.5698121 -0.021502593 0.15766286 0.31698872
## 19 1 1.232847e-02 0.5812500 -0.014015091 0.16083783 0.32392801
## 20 1 1.417474e-02 0.5988971 -0.014635079 0.14685904 0.32592837
## 21 1 1.629751e-02 0.6033905 -0.034967158 0.11553317 0.26529635
## 22 1 1.873817e-02 0.6214052 -0.007976591 0.11249647 0.26600570
## 23 1 2.154435e-02 0.6265931 -0.011557564 0.09504912 0.22355406
## 24 1 2.477076e-02 0.6386846 -0.003333412 0.07786177 0.18889198
## 25 1 2.848036e-02 0.6445670 0.003615448 0.07056963 0.17997073
## 26 1 3.274549e-02 0.6560049 0.002183099 0.06642828 0.15338785
## 27 1 3.764936e-02 0.6615196 -0.012102991 0.05004643 0.11327282
## 28 1 4.328761e-02 0.6615196 -0.012102991 0.05004643 0.11327282
## 29 1 4.977024e-02 0.6674020 -0.002425572 0.05159105 0.10929945
## 30 1 5.722368e-02 0.6729167 -0.009677419 0.02477693 0.03060269
## 31 1 6.579332e-02 0.6787990 0.000000000 0.02493805 0.00000000
## 32 1 7.564633e-02 0.6787990 0.000000000 0.02493805 0.00000000
## 33 1 8.697490e-02 0.6787990 0.000000000 0.02493805 0.00000000
## 34 1 1.000000e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 35 1 1.149757e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 36 1 1.321941e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 37 1 1.519911e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 38 1 1.747528e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 39 1 2.009233e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 40 1 2.310130e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 41 1 2.656088e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 42 1 3.053856e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 43 1 3.511192e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 44 1 4.037017e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 45 1 4.641589e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 46 1 5.336699e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 47 1 6.135907e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 48 1 7.054802e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 49 1 8.111308e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 50 1 9.326033e-01 0.6787990 0.000000000 0.02493805 0.00000000
## 51 1 1.072267e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 52 1 1.232847e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 53 1 1.417474e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 54 1 1.629751e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 55 1 1.873817e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 56 1 2.154435e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 57 1 2.477076e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 58 1 2.848036e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 59 1 3.274549e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 60 1 3.764936e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 61 1 4.328761e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 62 1 4.977024e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 63 1 5.722368e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 64 1 6.579332e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 65 1 7.564633e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 66 1 8.697490e+00 0.6787990 0.000000000 0.02493805 0.00000000
## 67 1 1.000000e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 68 1 1.149757e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 69 1 1.321941e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 70 1 1.519911e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 71 1 1.747528e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 72 1 2.009233e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 73 1 2.310130e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 74 1 2.656088e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 75 1 3.053856e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 76 1 3.511192e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 77 1 4.037017e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 78 1 4.641589e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 79 1 5.336699e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 80 1 6.135907e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 81 1 7.054802e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 82 1 8.111308e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 83 1 9.326033e+01 0.6787990 0.000000000 0.02493805 0.00000000
## 84 1 1.072267e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 85 1 1.232847e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 86 1 1.417474e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 87 1 1.629751e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 88 1 1.873817e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 89 1 2.154435e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 90 1 2.477076e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 91 1 2.848036e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 92 1 3.274549e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 93 1 3.764936e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 94 1 4.328761e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 95 1 4.977024e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 96 1 5.722368e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 97 1 6.579332e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 98 1 7.564633e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 99 1 8.697490e+02 0.6787990 0.000000000 0.02493805 0.00000000
## 100 1 1.000000e+03 0.6787990 0.000000000 0.02493805 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 0.0 0.0 0.0
## 2 28.6 67.9 3.6
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.6786
varImp(model_lasso, scale = FALSE)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q414 0 0 0
## race43 0 0 0
## race44 0 0 0
## age7 0 0 0
## q475 0 0 0
## sex2 0 0 0
## q492 0 0 0
## q895 0 0 0
## q493 0 0 0
## q174 0 0 0
## q262 0 0 0
## q413 0 0 0
## age5 0 0 0
## q173 0 0 0
## q872 0 0 0
## age4 0 0 0
## grade4 0 0 0
## q496 0 0 0
## q896 0 0 0
## q495 0 0 0
plot(varImp(model_lasso))
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 50) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 50) %>%
select(x2) %>%
arrange(desc(x2))
## [1] x2
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 50) %>%
select(x3) %>%
arrange(desc(x3))
## [1] x3
## <0 rows> (or 0-length row.names)
Chicago, IL (CH)
df_ch = df_fit_district %>%
filter(sitename == "Chicago, IL (CH)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ch,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 31 1 0.06579332
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5095833 0.0498295022 0.07765491 0.1409245
## 2 1 1.149757e-03 0.5029167 0.0411669936 0.07166048 0.1338934
## 3 1 1.321941e-03 0.4966667 0.0247954863 0.06188899 0.1159450
## 4 1 1.519911e-03 0.4966667 0.0247954863 0.06188899 0.1159450
## 5 1 1.747528e-03 0.4966667 0.0201171237 0.06188899 0.1108077
## 6 1 2.009233e-03 0.5029167 0.0239347875 0.07748083 0.1356542
## 7 1 2.310130e-03 0.5154167 0.0418695702 0.09602537 0.1601455
## 8 1 2.656088e-03 0.5154167 0.0418695702 0.09602537 0.1601455
## 9 1 3.053856e-03 0.5091667 0.0359872172 0.09464440 0.1605335
## 10 1 3.511192e-03 0.5087500 0.0364604365 0.10609238 0.1768424
## 11 1 4.037017e-03 0.5216667 0.0510453332 0.11063788 0.1834184
## 12 1 4.641589e-03 0.5283333 0.0526524983 0.10895123 0.1787328
## 13 1 5.336699e-03 0.5408333 0.0698248123 0.11992474 0.2009180
## 14 1 6.135907e-03 0.5345833 0.0467826682 0.11663442 0.1941076
## 15 1 7.054802e-03 0.5408333 0.0431979256 0.09577897 0.1668315
## 16 1 8.111308e-03 0.5341667 0.0295586206 0.10818722 0.2021062
## 17 1 9.326033e-03 0.5408333 0.0335693157 0.09577897 0.1936579
## 18 1 1.072267e-02 0.5529167 0.0358402319 0.10465718 0.2073551
## 19 1 1.232847e-02 0.5720833 0.0561322054 0.10132323 0.2026559
## 20 1 1.417474e-02 0.5591667 0.0158992201 0.10529573 0.1930816
## 21 1 1.629751e-02 0.5650000 0.0005508962 0.10853258 0.1980581
## 22 1 1.873817e-02 0.5650000 0.0005508962 0.10853258 0.1980581
## 23 1 2.154435e-02 0.5716667 0.0111181932 0.11288173 0.2027024
## 24 1 2.477076e-02 0.5716667 -0.0023654789 0.11288173 0.2089249
## 25 1 2.848036e-02 0.5916667 0.0129387634 0.09930313 0.1880298
## 26 1 3.274549e-02 0.6045833 0.0208892864 0.06234394 0.1253779
## 27 1 3.764936e-02 0.6175000 0.0439131980 0.07045925 0.1345301
## 28 1 4.328761e-02 0.6245833 0.0460371262 0.05239076 0.1123193
## 29 1 4.977024e-02 0.6245833 0.0460371262 0.05239076 0.1123193
## 30 1 5.722368e-02 0.6370833 0.0677301063 0.05082081 0.1211111
## 31 1 6.579332e-02 0.6495833 0.0890787118 0.04553565 0.1158773
## 32 1 7.564633e-02 0.6429167 0.0631527859 0.03573792 0.1017076
## 33 1 8.697490e-02 0.6241667 0.0000000000 0.01819391 0.0000000
## 34 1 1.000000e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 35 1 1.149757e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 36 1 1.321941e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 37 1 1.519911e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 38 1 1.747528e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 39 1 2.009233e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 40 1 2.310130e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 41 1 2.656088e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 42 1 3.053856e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 43 1 3.511192e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 44 1 4.037017e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 45 1 4.641589e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 46 1 5.336699e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 47 1 6.135907e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 48 1 7.054802e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 49 1 8.111308e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 50 1 9.326033e-01 0.6241667 0.0000000000 0.01819391 0.0000000
## 51 1 1.072267e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 52 1 1.232847e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 53 1 1.417474e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 54 1 1.629751e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 55 1 1.873817e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 56 1 2.154435e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 57 1 2.477076e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 58 1 2.848036e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 59 1 3.274549e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 60 1 3.764936e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 61 1 4.328761e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 62 1 4.977024e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 63 1 5.722368e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 64 1 6.579332e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 65 1 7.564633e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 66 1 8.697490e+00 0.6241667 0.0000000000 0.01819391 0.0000000
## 67 1 1.000000e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 68 1 1.149757e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 69 1 1.321941e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 70 1 1.519911e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 71 1 1.747528e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 72 1 2.009233e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 73 1 2.310130e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 74 1 2.656088e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 75 1 3.053856e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 76 1 3.511192e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 77 1 4.037017e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 78 1 4.641589e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 79 1 5.336699e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 80 1 6.135907e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 81 1 7.054802e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 82 1 8.111308e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 83 1 9.326033e+01 0.6241667 0.0000000000 0.01819391 0.0000000
## 84 1 1.072267e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 85 1 1.232847e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 86 1 1.417474e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 87 1 1.629751e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 88 1 1.873817e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 89 1 2.154435e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 90 1 2.477076e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 91 1 2.848036e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 92 1 3.274549e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 93 1 3.764936e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 94 1 4.328761e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 95 1 4.977024e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 96 1 5.722368e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 97 1 6.579332e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 98 1 7.564633e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 99 1 8.697490e+02 0.6241667 0.0000000000 0.01819391 0.0000000
## 100 1 1.000000e+03 0.6241667 0.0000000000 0.01819391 0.0000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 2.5 0.0 0.0
## 2 28.0 62.4 7.0
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.6497
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q494 100.000 0.00 0
## q414 0.000 15.28 0
## age6 7.837 0.00 0
## age5 2.203 0.00 0
## q417 0.000 0.00 0
## bmi 0.000 0.00 0
## qnothhpl2 0.000 0.00 0
## grade2 0.000 0.00 0
## q493 0.000 0.00 0
## grade3 0.000 0.00 0
## q496 0.000 0.00 0
## q502 0.000 0.00 0
## q176 0.000 0.00 0
## q412 0.000 0.00 0
## q416 0.000 0.00 0
## age7 0.000 0.00 0
## q175 0.000 0.00 0
## q894 0.000 0.00 0
## age4 0.000 0.00 0
## race43 0.000 0.00 0
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## q494 100
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## q414 15.28388
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## [1] x3
## <0 rows> (or 0-length row.names)
Eaton Consortium, MI (EA)
df_ea = df_fit_district %>%
filter(sitename == "Eaton Consortium, MI (EA)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ea,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
## Warning: from glmnet C++ code (error code -67); Convergence for 67th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -80); Convergence for 80th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -95); Convergence for 95th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -81); Convergence for 81th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -86); Convergence for 86th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -90); Convergence for 90th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
summary(model_lasso)
## Length Class Mode
## a0 267 -none- numeric
## beta 3 -none- list
## dfmat 267 -none- numeric
## df 89 -none- numeric
## dim 2 -none- numeric
## lambda 89 -none- numeric
## dev.ratio 89 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 100 1 1000
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5814103 -0.07245085 0.08779165 0.18592575
## 2 1 1.149757e-03 0.5814103 -0.07245085 0.08779165 0.18592575
## 3 1 1.321941e-03 0.5974359 -0.04837678 0.09029137 0.21499156
## 4 1 1.519911e-03 0.5974359 -0.04837678 0.09029137 0.21499156
## 5 1 1.747528e-03 0.6057692 -0.04087678 0.10064307 0.21037305
## 6 1 2.009233e-03 0.6134615 -0.04416795 0.10433172 0.23293960
## 7 1 2.310130e-03 0.6057692 -0.07680865 0.09388345 0.18460487
## 8 1 2.656088e-03 0.5980769 -0.10123185 0.08903105 0.12656590
## 9 1 3.053856e-03 0.5980769 -0.10123185 0.08903105 0.12656590
## 10 1 3.511192e-03 0.5903846 -0.10656257 0.09765916 0.12756103
## 11 1 4.037017e-03 0.5903846 -0.12808643 0.11030485 0.11046953
## 12 1 4.641589e-03 0.5903846 -0.12808643 0.11030485 0.11046953
## 13 1 5.336699e-03 0.5987179 -0.12159556 0.10577247 0.10483643
## 14 1 6.135907e-03 0.6212454 -0.11740168 0.09422716 0.09119244
## 15 1 7.054802e-03 0.6135531 -0.13385737 0.11045591 0.11083977
## 16 1 8.111308e-03 0.6289377 -0.12137120 0.09680026 0.12692401
## 17 1 9.326033e-03 0.6360806 -0.11703085 0.08616519 0.12898942
## 18 1 1.072267e-02 0.6360806 -0.11005779 0.10662651 0.16347636
## 19 1 1.232847e-02 0.6437729 -0.10251427 0.10773649 0.16152592
## 20 1 1.417474e-02 0.6674908 -0.05092557 0.10586374 0.19443621
## 21 1 1.629751e-02 0.6674908 -0.05092557 0.10586374 0.19443621
## 22 1 1.873817e-02 0.6674908 -0.05092557 0.10586374 0.19443621
## 23 1 2.154435e-02 0.6752747 -0.03529854 0.11670562 0.19694302
## 24 1 2.477076e-02 0.6829670 -0.02779583 0.10896435 0.18924696
## 25 1 2.848036e-02 0.6823260 -0.06282398 0.08342938 0.14873974
## 26 1 3.274549e-02 0.6977106 -0.07063830 0.09139833 0.09511376
## 27 1 3.764936e-02 0.7451465 -0.02467339 0.05895687 0.05262862
## 28 1 4.328761e-02 0.7528388 -0.01403509 0.05625300 0.04438284
## 29 1 4.977024e-02 0.7528388 -0.01403509 0.05625300 0.04438284
## 30 1 5.722368e-02 0.7528388 -0.01403509 0.05625300 0.04438284
## 31 1 6.579332e-02 0.7528388 -0.01403509 0.05625300 0.04438284
## 32 1 7.564633e-02 0.7682234 0.00000000 0.02884397 0.00000000
## 33 1 8.697490e-02 0.7682234 0.00000000 0.02884397 0.00000000
## 34 1 1.000000e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 35 1 1.149757e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 36 1 1.321941e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 37 1 1.519911e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 38 1 1.747528e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 39 1 2.009233e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 40 1 2.310130e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 41 1 2.656088e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 42 1 3.053856e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 43 1 3.511192e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 44 1 4.037017e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 45 1 4.641589e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 46 1 5.336699e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 47 1 6.135907e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 48 1 7.054802e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 49 1 8.111308e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 50 1 9.326033e-01 0.7682234 0.00000000 0.02884397 0.00000000
## 51 1 1.072267e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 52 1 1.232847e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 53 1 1.417474e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 54 1 1.629751e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 55 1 1.873817e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 56 1 2.154435e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 57 1 2.477076e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 58 1 2.848036e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 59 1 3.274549e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 60 1 3.764936e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 61 1 4.328761e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 62 1 4.977024e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 63 1 5.722368e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 64 1 6.579332e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 65 1 7.564633e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 66 1 8.697490e+00 0.7682234 0.00000000 0.02884397 0.00000000
## 67 1 1.000000e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 68 1 1.149757e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 69 1 1.321941e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 70 1 1.519911e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 71 1 1.747528e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 72 1 2.009233e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 73 1 2.310130e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 74 1 2.656088e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 75 1 3.053856e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 76 1 3.511192e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 77 1 4.037017e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 78 1 4.641589e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 79 1 5.336699e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 80 1 6.135907e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 81 1 7.054802e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 82 1 8.111308e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 83 1 9.326033e+01 0.7682234 0.00000000 0.02884397 0.00000000
## 84 1 1.072267e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 85 1 1.232847e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 86 1 1.417474e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 87 1 1.629751e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 88 1 1.873817e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 89 1 2.154435e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 90 1 2.477076e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 91 1 2.848036e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 92 1 3.274549e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 93 1 3.764936e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 94 1 4.328761e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 95 1 4.977024e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 96 1 5.722368e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 97 1 6.579332e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 98 1 7.564633e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 99 1 8.697490e+02 0.7682234 0.00000000 0.02884397 0.00000000
## 100 1 1.000000e+03 0.7682234 0.00000000 0.02884397 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 0.0 0.0 0.0
## 2 16.3 76.7 7.0
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.7674
varImp(model_lasso, scale = FALSE)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q414 0 0 0
## race43 0 0 0
## race44 0 0 0
## age7 0 0 0
## q475 0 0 0
## sex2 0 0 0
## q492 0 0 0
## q895 0 0 0
## q493 0 0 0
## q174 0 0 0
## q262 0 0 0
## q413 0 0 0
## age5 0 0 0
## q173 0 0 0
## q872 0 0 0
## age4 0 0 0
## grade4 0 0 0
## q496 0 0 0
## q896 0 0 0
## q495 0 0 0
plot(varImp(model_lasso))
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## [1] x2
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## [1] x3
## <0 rows> (or 0-length row.names)
Fort Worth, TX (FW)
df_fw = df_fit_district %>%
filter(sitename == "Fort Worth, TX (FW)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_fw,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 279 -none- numeric
## beta 3 -none- list
## dfmat 279 -none- numeric
## df 93 -none- numeric
## dim 2 -none- numeric
## lambda 93 -none- numeric
## dev.ratio 93 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 100 1 1000
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.6662688 0.088659183 0.04353769 0.16185524
## 2 1 1.149757e-03 0.6662688 0.088659183 0.04353769 0.16185524
## 3 1 1.321941e-03 0.6747833 0.099861540 0.03577065 0.15535385
## 4 1 1.519911e-03 0.6747833 0.099861540 0.03577065 0.15535385
## 5 1 1.747528e-03 0.6660711 0.062462888 0.04027119 0.15387385
## 6 1 2.009233e-03 0.6704190 0.065972928 0.04094547 0.14989115
## 7 1 2.310130e-03 0.6745856 0.071329427 0.04260750 0.17500844
## 8 1 2.656088e-03 0.6745856 0.054283640 0.04260750 0.18983757
## 9 1 3.053856e-03 0.6791311 0.057040660 0.04044663 0.18695364
## 10 1 3.511192e-03 0.6791311 0.057040660 0.04044663 0.18695364
## 11 1 4.037017e-03 0.6836456 0.053707375 0.04290291 0.19075310
## 12 1 4.641589e-03 0.6836456 0.053707375 0.04290291 0.19075310
## 13 1 5.336699e-03 0.6794789 0.035513572 0.04225732 0.17043718
## 14 1 6.135907e-03 0.6794789 0.026215353 0.03741495 0.15158904
## 15 1 7.054802e-03 0.6753123 0.009875484 0.03614303 0.13613222
## 16 1 8.111308e-03 0.6709644 -0.008487423 0.04104432 0.14268372
## 17 1 9.326033e-03 0.6751311 0.008290707 0.04264333 0.15027465
## 18 1 1.072267e-02 0.6879934 0.028069986 0.04632172 0.15523204
## 19 1 1.232847e-02 0.7007055 0.045425865 0.05119863 0.16202602
## 20 1 1.417474e-02 0.7048722 0.036119027 0.04979695 0.15275065
## 21 1 1.629751e-02 0.7048722 0.025729417 0.04979695 0.15115344
## 22 1 1.873817e-02 0.7048722 0.025729417 0.04979695 0.15115344
## 23 1 2.154435e-02 0.7048722 -0.002628811 0.03636384 0.10922225
## 24 1 2.477076e-02 0.7048722 -0.032343372 0.02346750 0.05193089
## 25 1 2.848036e-02 0.7090389 -0.027044579 0.01925045 0.04460088
## 26 1 3.274549e-02 0.7007055 -0.050725725 0.03283428 0.04849524
## 27 1 3.764936e-02 0.7007055 -0.050725725 0.03283428 0.04849524
## 28 1 4.328761e-02 0.7137655 -0.031487342 0.03775502 0.04438190
## 29 1 4.977024e-02 0.7220988 -0.018829114 0.04041644 0.04227609
## 30 1 5.722368e-02 0.7345988 0.000000000 0.02233381 0.00000000
## 31 1 6.579332e-02 0.7345988 0.000000000 0.02233381 0.00000000
## 32 1 7.564633e-02 0.7345988 0.000000000 0.02233381 0.00000000
## 33 1 8.697490e-02 0.7345988 0.000000000 0.02233381 0.00000000
## 34 1 1.000000e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 35 1 1.149757e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 36 1 1.321941e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 37 1 1.519911e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 38 1 1.747528e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 39 1 2.009233e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 40 1 2.310130e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 41 1 2.656088e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 42 1 3.053856e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 43 1 3.511192e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 44 1 4.037017e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 45 1 4.641589e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 46 1 5.336699e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 47 1 6.135907e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 48 1 7.054802e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 49 1 8.111308e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 50 1 9.326033e-01 0.7345988 0.000000000 0.02233381 0.00000000
## 51 1 1.072267e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 52 1 1.232847e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 53 1 1.417474e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 54 1 1.629751e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 55 1 1.873817e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 56 1 2.154435e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 57 1 2.477076e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 58 1 2.848036e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 59 1 3.274549e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 60 1 3.764936e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 61 1 4.328761e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 62 1 4.977024e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 63 1 5.722368e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 64 1 6.579332e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 65 1 7.564633e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 66 1 8.697490e+00 0.7345988 0.000000000 0.02233381 0.00000000
## 67 1 1.000000e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 68 1 1.149757e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 69 1 1.321941e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 70 1 1.519911e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 71 1 1.747528e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 72 1 2.009233e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 73 1 2.310130e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 74 1 2.656088e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 75 1 3.053856e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 76 1 3.511192e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 77 1 4.037017e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 78 1 4.641589e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 79 1 5.336699e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 80 1 6.135907e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 81 1 7.054802e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 82 1 8.111308e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 83 1 9.326033e+01 0.7345988 0.000000000 0.02233381 0.00000000
## 84 1 1.072267e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 85 1 1.232847e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 86 1 1.417474e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 87 1 1.629751e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 88 1 1.873817e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 89 1 2.154435e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 90 1 2.477076e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 91 1 2.848036e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 92 1 3.274549e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 93 1 3.764936e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 94 1 4.328761e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 95 1 4.977024e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 96 1 5.722368e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 97 1 6.579332e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 98 1 7.564633e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 99 1 8.697490e+02 0.7345988 0.000000000 0.02233381 0.00000000
## 100 1 1.000000e+03 0.7345988 0.000000000 0.02233381 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 0.0 0.0 0.0
## 2 19.0 73.4 7.6
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.7342
varImp(model_lasso, scale = FALSE)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q414 0 0 0
## race43 0 0 0
## race44 0 0 0
## age7 0 0 0
## q475 0 0 0
## sex2 0 0 0
## q492 0 0 0
## q895 0 0 0
## q493 0 0 0
## q174 0 0 0
## q262 0 0 0
## q413 0 0 0
## age5 0 0 0
## q173 0 0 0
## q872 0 0 0
## age4 0 0 0
## grade4 0 0 0
## q496 0 0 0
## q896 0 0 0
## q495 0 0 0
plot(varImp(model_lasso))
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## [1] x2
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## [1] x3
## <0 rows> (or 0-length row.names)
Genesee Consortium, MI (GE)
df_ge = df_fit_district %>%
filter(sitename == "Genesee Consortium, MI (GE)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ge,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 23 1 0.02154435
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.6406618 0.109877494 0.08604027 0.19876689
## 2 1 1.149757e-03 0.6418137 0.120398707 0.11567561 0.23191382
## 3 1 1.321941e-03 0.6418137 0.120398707 0.11567561 0.23191382
## 4 1 1.519911e-03 0.6539461 0.132076445 0.12453041 0.23975486
## 5 1 1.747528e-03 0.6539461 0.132076445 0.12453041 0.23975486
## 6 1 2.009233e-03 0.6598284 0.137976445 0.12245358 0.24028674
## 7 1 2.310130e-03 0.6715931 0.149656231 0.11388317 0.23868310
## 8 1 2.656088e-03 0.6715931 0.139555220 0.11388317 0.22159776
## 9 1 3.053856e-03 0.6657108 0.133556632 0.12318270 0.23372563
## 10 1 3.511192e-03 0.6719608 0.140964039 0.12596520 0.24179511
## 11 1 4.037017e-03 0.6719608 0.130974723 0.12596520 0.25294136
## 12 1 4.641589e-03 0.6660784 0.099478475 0.12860314 0.26667735
## 13 1 5.336699e-03 0.6660784 0.099478475 0.12860314 0.26667735
## 14 1 6.135907e-03 0.6837255 0.119028983 0.12272902 0.26852736
## 15 1 7.054802e-03 0.6770588 0.074584538 0.11312213 0.24437723
## 16 1 8.111308e-03 0.6829412 0.096553769 0.10219430 0.21420492
## 17 1 9.326033e-03 0.6829412 0.088022443 0.10219430 0.22698200
## 18 1 1.072267e-02 0.6888235 0.095113042 0.09371647 0.22192348
## 19 1 1.232847e-02 0.6888235 0.095113042 0.09371647 0.22192348
## 20 1 1.417474e-02 0.6888235 0.095113042 0.09371647 0.22192348
## 21 1 1.629751e-02 0.7005882 0.112664027 0.08681648 0.21898420
## 22 1 1.873817e-02 0.7252206 0.180079250 0.10476102 0.30472510
## 23 1 2.154435e-02 0.7428676 0.198625045 0.08473025 0.27541555
## 24 1 2.477076e-02 0.7186029 0.087062502 0.06181920 0.18635112
## 25 1 2.848036e-02 0.7127206 0.077385083 0.06583248 0.19374100
## 26 1 3.274549e-02 0.7002206 -0.019941349 0.04853690 0.05998483
## 27 1 3.764936e-02 0.7064706 -0.009032258 0.05069301 0.08646124
## 28 1 4.328761e-02 0.7002206 -0.040143369 0.04853690 0.05198986
## 29 1 4.977024e-02 0.7119853 -0.020788530 0.03976341 0.04395616
## 30 1 5.722368e-02 0.7178676 -0.011111111 0.03283957 0.03513642
## 31 1 6.579332e-02 0.7241176 0.000000000 0.03236185 0.00000000
## 32 1 7.564633e-02 0.7241176 0.000000000 0.03236185 0.00000000
## 33 1 8.697490e-02 0.7241176 0.000000000 0.03236185 0.00000000
## 34 1 1.000000e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 35 1 1.149757e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 36 1 1.321941e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 37 1 1.519911e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 38 1 1.747528e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 39 1 2.009233e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 40 1 2.310130e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 41 1 2.656088e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 42 1 3.053856e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 43 1 3.511192e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 44 1 4.037017e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 45 1 4.641589e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 46 1 5.336699e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 47 1 6.135907e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 48 1 7.054802e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 49 1 8.111308e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 50 1 9.326033e-01 0.7241176 0.000000000 0.03236185 0.00000000
## 51 1 1.072267e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 52 1 1.232847e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 53 1 1.417474e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 54 1 1.629751e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 55 1 1.873817e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 56 1 2.154435e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 57 1 2.477076e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 58 1 2.848036e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 59 1 3.274549e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 60 1 3.764936e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 61 1 4.328761e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 62 1 4.977024e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 63 1 5.722368e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 64 1 6.579332e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 65 1 7.564633e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 66 1 8.697490e+00 0.7241176 0.000000000 0.03236185 0.00000000
## 67 1 1.000000e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 68 1 1.149757e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 69 1 1.321941e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 70 1 1.519911e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 71 1 1.747528e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 72 1 2.009233e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 73 1 2.310130e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 74 1 2.656088e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 75 1 3.053856e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 76 1 3.511192e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 77 1 4.037017e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 78 1 4.641589e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 79 1 5.336699e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 80 1 6.135907e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 81 1 7.054802e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 82 1 8.111308e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 83 1 9.326033e+01 0.7241176 0.000000000 0.03236185 0.00000000
## 84 1 1.072267e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 85 1 1.232847e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 86 1 1.417474e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 87 1 1.629751e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 88 1 1.873817e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 89 1 2.154435e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 90 1 2.477076e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 91 1 2.848036e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 92 1 3.274549e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 93 1 3.764936e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 94 1 4.328761e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 95 1 4.977024e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 96 1 5.722368e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 97 1 6.579332e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 98 1 7.564633e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 99 1 8.697490e+02 0.7241176 0.000000000 0.03236185 0.00000000
## 100 1 1.000000e+03 0.7241176 0.000000000 0.03236185 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 6.0 4.2 0.6
## 2 16.9 68.1 4.2
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.741
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q177 100.00 0.00 0.00
## q532 89.72 0.00 0.00
## q496 0.00 72.61 0.00
## q415 0.00 72.05 0.00
## q213 72.04 0.00 0.00
## q215 67.37 0.00 0.00
## q494 18.93 0.00 43.78
## q175 0.00 42.66 0.00
## q894 0.00 39.12 0.00
## q872 35.14 0.00 0.00
## q654 34.62 0.00 0.00
## q414 0.00 33.77 0.00
## qnothhpl2 0.00 32.85 0.00
## q412 0.00 32.56 0.00
## q232 31.05 0.00 0.00
## q493 0.00 27.81 0.00
## q897 0.00 27.60 0.00
## q474 0.00 25.29 0.00
## q492 0.00 25.03 0.00
## q174 0.00 0.00 22.79
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## q177 100.00000
## q532 89.72323
## q213 72.04383
## q215 67.36966
## q872 35.13896
## q654 34.61790
## q232 31.04861
## q494 18.93238
## q172 18.33631
## grade2 16.35997
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## q496 72.61062
## q415 72.04780
## q175 42.66361
## q894 39.12014
## q414 33.77327
## qnothhpl2 32.85212
## q412 32.56322
## q493 27.81219
## q897 27.59926
## q474 25.28549
## q492 25.02565
## q472 21.73536
## q522 18.97933
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## x3
## q494 43.77922
## q174 22.78910
Hillsborough County, FL (HL)
df_hl = df_fit_district %>%
filter(sitename == "Hillsborough County, FL (HL)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_hl,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 288 -none- numeric
## beta 3 -none- list
## dfmat 288 -none- numeric
## df 96 -none- numeric
## dim 2 -none- numeric
## lambda 96 -none- numeric
## dev.ratio 96 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 100 1 1000
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.6975764 0.075786212 0.06855044 0.13990379
## 2 1 1.149757e-03 0.7017430 0.082529138 0.07578905 0.14821735
## 3 1 1.321941e-03 0.7097559 0.081922099 0.07237873 0.14656233
## 4 1 1.519911e-03 0.7097559 0.081922099 0.07237873 0.14656233
## 5 1 1.747528e-03 0.7137559 0.099779241 0.06650619 0.12966266
## 6 1 2.009233e-03 0.7219225 0.105008188 0.07106512 0.12710278
## 7 1 2.310130e-03 0.7222430 0.107025917 0.07657302 0.13044676
## 8 1 2.656088e-03 0.7222430 0.107025917 0.07657302 0.13044676
## 9 1 3.053856e-03 0.7427977 0.132796874 0.06941435 0.13084309
## 10 1 3.511192e-03 0.7509643 0.149662977 0.07077582 0.14590194
## 11 1 4.037017e-03 0.7589771 0.160709577 0.06675436 0.14290446
## 12 1 4.641589e-03 0.7551310 0.140549150 0.07192981 0.17100400
## 13 1 5.336699e-03 0.7509643 0.107186054 0.07077582 0.18450883
## 14 1 6.135907e-03 0.7549643 0.110927252 0.06995697 0.18031344
## 15 1 7.054802e-03 0.7512993 0.062099342 0.06667684 0.18471450
## 16 1 8.111308e-03 0.7512993 0.062099342 0.06667684 0.18471450
## 17 1 9.326033e-03 0.7594933 0.074705662 0.06980430 0.18764402
## 18 1 1.072267e-02 0.7591455 0.046710312 0.05962167 0.17948889
## 19 1 1.232847e-02 0.7708378 0.062030160 0.04542368 0.17881947
## 20 1 1.417474e-02 0.7745173 0.050081423 0.02969109 0.13715536
## 21 1 1.629751e-02 0.7746711 0.050866517 0.03832167 0.14763651
## 22 1 1.873817e-02 0.7709916 0.007460833 0.04435393 0.13105877
## 23 1 2.154435e-02 0.7748378 0.012285178 0.03798273 0.12723463
## 24 1 2.477076e-02 0.7791856 -0.003928376 0.03675934 0.07762052
## 25 1 2.848036e-02 0.7750190 -0.030595043 0.03754058 0.03242548
## 26 1 3.274549e-02 0.7791856 -0.023928376 0.03675934 0.03100745
## 27 1 3.764936e-02 0.7911984 -0.005813953 0.02951882 0.01838534
## 28 1 4.328761e-02 0.7950446 0.000000000 0.02242322 0.00000000
## 29 1 4.977024e-02 0.7950446 0.000000000 0.02242322 0.00000000
## 30 1 5.722368e-02 0.7950446 0.000000000 0.02242322 0.00000000
## 31 1 6.579332e-02 0.7950446 0.000000000 0.02242322 0.00000000
## 32 1 7.564633e-02 0.7950446 0.000000000 0.02242322 0.00000000
## 33 1 8.697490e-02 0.7950446 0.000000000 0.02242322 0.00000000
## 34 1 1.000000e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 35 1 1.149757e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 36 1 1.321941e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 37 1 1.519911e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 38 1 1.747528e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 39 1 2.009233e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 40 1 2.310130e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 41 1 2.656088e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 42 1 3.053856e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 43 1 3.511192e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 44 1 4.037017e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 45 1 4.641589e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 46 1 5.336699e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 47 1 6.135907e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 48 1 7.054802e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 49 1 8.111308e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 50 1 9.326033e-01 0.7950446 0.000000000 0.02242322 0.00000000
## 51 1 1.072267e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 52 1 1.232847e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 53 1 1.417474e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 54 1 1.629751e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 55 1 1.873817e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 56 1 2.154435e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 57 1 2.477076e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 58 1 2.848036e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 59 1 3.274549e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 60 1 3.764936e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 61 1 4.328761e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 62 1 4.977024e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 63 1 5.722368e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 64 1 6.579332e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 65 1 7.564633e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 66 1 8.697490e+00 0.7950446 0.000000000 0.02242322 0.00000000
## 67 1 1.000000e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 68 1 1.149757e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 69 1 1.321941e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 70 1 1.519911e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 71 1 1.747528e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 72 1 2.009233e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 73 1 2.310130e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 74 1 2.656088e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 75 1 3.053856e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 76 1 3.511192e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 77 1 4.037017e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 78 1 4.641589e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 79 1 5.336699e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 80 1 6.135907e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 81 1 7.054802e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 82 1 8.111308e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 83 1 9.326033e+01 0.7950446 0.000000000 0.02242322 0.00000000
## 84 1 1.072267e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 85 1 1.232847e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 86 1 1.417474e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 87 1 1.629751e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 88 1 1.873817e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 89 1 2.154435e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 90 1 2.477076e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 91 1 2.848036e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 92 1 3.274549e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 93 1 3.764936e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 94 1 4.328761e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 95 1 4.977024e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 96 1 5.722368e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 97 1 6.579332e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 98 1 7.564633e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 99 1 8.697490e+02 0.7950446 0.000000000 0.02242322 0.00000000
## 100 1 1.000000e+03 0.7950446 0.000000000 0.02242322 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 0.0 0.0 0.0
## 2 14.9 79.4 5.6
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.7944
varImp(model_lasso, scale = FALSE)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q414 0 0 0
## race43 0 0 0
## race44 0 0 0
## age7 0 0 0
## q475 0 0 0
## sex2 0 0 0
## q492 0 0 0
## q895 0 0 0
## q493 0 0 0
## q174 0 0 0
## q262 0 0 0
## q413 0 0 0
## age5 0 0 0
## q173 0 0 0
## q872 0 0 0
## age4 0 0 0
## grade4 0 0 0
## q496 0 0 0
## q896 0 0 0
## q495 0 0 0
plot(varImp(model_lasso))
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 50) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 50) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 50) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
Los Angeles, CA (LO)
df_lo = df_fit_district %>%
filter(sitename == "Los Angeles, CA (LO)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_lo,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning: from glmnet C++ code (error code -74); Convergence for 74th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning: from glmnet C++ code (error code -89); Convergence for 89th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning: from glmnet C++ code (error code -93); Convergence for 93th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
summary(model_lasso)
## Length Class Mode
## a0 294 -none- numeric
## beta 3 -none- list
## dfmat 294 -none- numeric
## df 98 -none- numeric
## dim 2 -none- numeric
## lambda 98 -none- numeric
## dev.ratio 98 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 100 1 1000
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5975253 0.024156821 0.12138326 0.22412200
## 2 1 1.149757e-03 0.5975253 0.010360834 0.12138326 0.23375900
## 3 1 1.321941e-03 0.5884343 -0.006518961 0.10536904 0.19573045
## 4 1 1.519911e-03 0.5975253 0.008701834 0.09604298 0.19859885
## 5 1 1.747528e-03 0.5975253 0.008701834 0.09604298 0.19859885
## 6 1 2.009233e-03 0.5875253 0.006439391 0.08914616 0.20077332
## 7 1 2.310130e-03 0.5882828 0.002346657 0.09414878 0.20014573
## 8 1 2.656088e-03 0.5791919 -0.007277756 0.10274073 0.19357220
## 9 1 3.053856e-03 0.5625253 -0.039942640 0.12754007 0.17798859
## 10 1 3.511192e-03 0.5625253 -0.039942640 0.12754007 0.17798859
## 11 1 4.037017e-03 0.5716162 -0.035519504 0.12211268 0.17737675
## 12 1 4.641589e-03 0.5716162 -0.035519504 0.12211268 0.17737675
## 13 1 5.336699e-03 0.5525253 -0.066581365 0.13516747 0.18427779
## 14 1 6.135907e-03 0.5699495 -0.039822727 0.13818078 0.23447649
## 15 1 7.054802e-03 0.5775253 -0.036518979 0.11109868 0.19232054
## 16 1 8.111308e-03 0.5875253 -0.046278250 0.10779672 0.19466757
## 17 1 9.326033e-03 0.5875253 -0.041375288 0.12366579 0.21346262
## 18 1 1.072267e-02 0.5958586 -0.031259376 0.11986173 0.22192100
## 19 1 1.232847e-02 0.6041919 -0.050231173 0.09653852 0.21918039
## 20 1 1.417474e-02 0.6041919 -0.050231173 0.09653852 0.21918039
## 21 1 1.629751e-02 0.6041919 -0.043202065 0.11398588 0.26307528
## 22 1 1.873817e-02 0.6327273 -0.008594127 0.10992062 0.27491103
## 23 1 2.154435e-02 0.6327273 -0.053408197 0.10122250 0.22747642
## 24 1 2.477076e-02 0.6609091 -0.021666376 0.07887460 0.22076328
## 25 1 2.848036e-02 0.6965152 0.069974364 0.11033718 0.31657004
## 26 1 3.274549e-02 0.6874242 0.027869101 0.10267827 0.29171166
## 27 1 3.764936e-02 0.6874242 -0.004834453 0.08288385 0.25014727
## 28 1 4.328761e-02 0.6981818 0.002854394 0.08902022 0.21656244
## 29 1 4.977024e-02 0.6974242 -0.026043322 0.07115317 0.15658270
## 30 1 5.722368e-02 0.6890909 -0.058118794 0.06916353 0.10043477
## 31 1 6.579332e-02 0.7163636 -0.017391304 0.04098571 0.05499613
## 32 1 7.564633e-02 0.7274747 0.000000000 0.04107689 0.00000000
## 33 1 8.697490e-02 0.7274747 0.000000000 0.04107689 0.00000000
## 34 1 1.000000e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 35 1 1.149757e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 36 1 1.321941e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 37 1 1.519911e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 38 1 1.747528e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 39 1 2.009233e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 40 1 2.310130e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 41 1 2.656088e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 42 1 3.053856e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 43 1 3.511192e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 44 1 4.037017e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 45 1 4.641589e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 46 1 5.336699e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 47 1 6.135907e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 48 1 7.054802e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 49 1 8.111308e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 50 1 9.326033e-01 0.7274747 0.000000000 0.04107689 0.00000000
## 51 1 1.072267e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 52 1 1.232847e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 53 1 1.417474e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 54 1 1.629751e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 55 1 1.873817e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 56 1 2.154435e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 57 1 2.477076e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 58 1 2.848036e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 59 1 3.274549e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 60 1 3.764936e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 61 1 4.328761e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 62 1 4.977024e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 63 1 5.722368e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 64 1 6.579332e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 65 1 7.564633e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 66 1 8.697490e+00 0.7274747 0.000000000 0.04107689 0.00000000
## 67 1 1.000000e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 68 1 1.149757e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 69 1 1.321941e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 70 1 1.519911e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 71 1 1.747528e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 72 1 2.009233e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 73 1 2.310130e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 74 1 2.656088e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 75 1 3.053856e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 76 1 3.511192e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 77 1 4.037017e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 78 1 4.641589e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 79 1 5.336699e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 80 1 6.135907e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 81 1 7.054802e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 82 1 8.111308e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 83 1 9.326033e+01 0.7274747 0.000000000 0.04107689 0.00000000
## 84 1 1.072267e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 85 1 1.232847e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 86 1 1.417474e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 87 1 1.629751e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 88 1 1.873817e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 89 1 2.154435e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 90 1 2.477076e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 91 1 2.848036e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 92 1 3.274549e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 93 1 3.764936e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 94 1 4.328761e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 95 1 4.977024e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 96 1 5.722368e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 97 1 6.579332e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 98 1 7.564633e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 99 1 8.697490e+02 0.7274747 0.000000000 0.04107689 0.00000000
## 100 1 1.000000e+03 0.7274747 0.000000000 0.04107689 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 0.0 0.0 0.0
## 2 23.9 72.5 3.7
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.7248
varImp(model_lasso, scale = FALSE)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q414 0 0 0
## race43 0 0 0
## race44 0 0 0
## age7 0 0 0
## q475 0 0 0
## sex2 0 0 0
## q492 0 0 0
## q895 0 0 0
## q493 0 0 0
## q174 0 0 0
## q262 0 0 0
## q413 0 0 0
## age5 0 0 0
## q173 0 0 0
## q872 0 0 0
## age4 0 0 0
## grade4 0 0 0
## q496 0 0 0
## q896 0 0 0
## q495 0 0 0
plot(varImp(model_lasso))
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 50) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 50) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 50) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
Newark, NJ (NW)
df_nw = df_fit_district %>%
filter(sitename == "Newark, NJ (NW)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_nw,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
## Warning: from glmnet C++ code (error code -68); Convergence for 68th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -84); Convergence for 84th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 33 1 0.0869749
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5051282 0.057279202 0.08483578 0.15958556
## 2 1 1.149757e-03 0.5051282 0.062006093 0.08483578 0.16478058
## 3 1 1.321941e-03 0.4979853 0.046300964 0.08157773 0.14811992
## 4 1 1.519911e-03 0.4908425 0.025870857 0.08445799 0.16797464
## 5 1 1.747528e-03 0.4979853 0.038605688 0.08825369 0.18522478
## 6 1 2.009233e-03 0.4979853 0.028205358 0.08825369 0.19104528
## 7 1 2.310130e-03 0.4979853 0.025047463 0.08825369 0.19182271
## 8 1 2.656088e-03 0.4984615 0.014793331 0.09061652 0.18637016
## 9 1 3.053856e-03 0.4841026 -0.001375342 0.11197469 0.20006649
## 10 1 3.511192e-03 0.4769597 -0.021537787 0.10799263 0.21537933
## 11 1 4.037017e-03 0.4769597 -0.031633941 0.10799263 0.22731127
## 12 1 4.641589e-03 0.4841026 -0.007957471 0.11197469 0.21703071
## 13 1 5.336699e-03 0.4912454 -0.003389490 0.12014434 0.22723143
## 14 1 6.135907e-03 0.4912454 0.002100707 0.12014434 0.22829900
## 15 1 7.054802e-03 0.4912454 -0.008315960 0.12014434 0.21112455
## 16 1 8.111308e-03 0.4917216 -0.015975699 0.11841839 0.19210349
## 17 1 9.326033e-03 0.4850549 -0.040092428 0.11341620 0.18081015
## 18 1 1.072267e-02 0.4783883 -0.064370569 0.11221606 0.18428595
## 19 1 1.232847e-02 0.4921978 -0.052249317 0.12142672 0.17658552
## 20 1 1.417474e-02 0.5064835 -0.042006121 0.12609190 0.18384246
## 21 1 1.629751e-02 0.4931502 -0.070486794 0.13731971 0.20769505
## 22 1 1.873817e-02 0.5064835 -0.051395885 0.14088926 0.21427894
## 23 1 2.154435e-02 0.5207692 -0.031283103 0.14335616 0.21521177
## 24 1 2.477076e-02 0.5402930 -0.004807241 0.12470111 0.20648582
## 25 1 2.848036e-02 0.5541026 0.016915448 0.13278488 0.22074964
## 26 1 3.274549e-02 0.5760806 0.048069829 0.11870331 0.19565182
## 27 1 3.764936e-02 0.5694139 0.009801146 0.11047737 0.16690627
## 28 1 4.328761e-02 0.5698901 -0.010225845 0.10888946 0.14755463
## 29 1 4.977024e-02 0.5755311 -0.020116391 0.11219464 0.18255691
## 30 1 5.722368e-02 0.6047253 0.014184571 0.07405439 0.12401023
## 31 1 6.579332e-02 0.6267033 0.039936914 0.04582724 0.10872151
## 32 1 7.564633e-02 0.6272527 0.027647059 0.02062892 0.06100844
## 33 1 8.697490e-02 0.6343956 0.035333333 0.03440124 0.08279381
## 34 1 1.000000e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 35 1 1.149757e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 36 1 1.321941e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 37 1 1.519911e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 38 1 1.747528e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 39 1 2.009233e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 40 1 2.310130e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 41 1 2.656088e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 42 1 3.053856e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 43 1 3.511192e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 44 1 4.037017e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 45 1 4.641589e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 46 1 5.336699e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 47 1 6.135907e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 48 1 7.054802e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 49 1 8.111308e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 50 1 9.326033e-01 0.6272527 0.000000000 0.02062892 0.00000000
## 51 1 1.072267e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 52 1 1.232847e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 53 1 1.417474e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 54 1 1.629751e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 55 1 1.873817e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 56 1 2.154435e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 57 1 2.477076e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 58 1 2.848036e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 59 1 3.274549e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 60 1 3.764936e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 61 1 4.328761e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 62 1 4.977024e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 63 1 5.722368e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 64 1 6.579332e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 65 1 7.564633e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 66 1 8.697490e+00 0.6272527 0.000000000 0.02062892 0.00000000
## 67 1 1.000000e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 68 1 1.149757e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 69 1 1.321941e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 70 1 1.519911e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 71 1 1.747528e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 72 1 2.009233e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 73 1 2.310130e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 74 1 2.656088e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 75 1 3.053856e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 76 1 3.511192e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 77 1 4.037017e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 78 1 4.641589e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 79 1 5.336699e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 80 1 6.135907e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 81 1 7.054802e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 82 1 8.111308e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 83 1 9.326033e+01 0.6272527 0.000000000 0.02062892 0.00000000
## 84 1 1.072267e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 85 1 1.232847e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 86 1 1.417474e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 87 1 1.629751e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 88 1 1.873817e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 89 1 2.154435e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 90 1 2.477076e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 91 1 2.848036e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 92 1 3.274549e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 93 1 3.764936e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 94 1 4.328761e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 95 1 4.977024e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 96 1 5.722368e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 97 1 6.579332e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 98 1 7.564633e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 99 1 8.697490e+02 0.6272527 0.000000000 0.02062892 0.00000000
## 100 1 1.000000e+03 0.6272527 0.000000000 0.02062892 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 1.4 0.7 0.0
## 2 26.8 62.0 9.2
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.6338
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q476 0.00 100 0.00
## q873 0.00 0 82.23
## qnothhpl2 23.34 0 0.00
## q494 10.09 0 0.00
## q415 0.00 0 0.00
## race43 0.00 0 0.00
## race44 0.00 0 0.00
## age7 0.00 0 0.00
## q492 0.00 0 0.00
## sex2 0.00 0 0.00
## q495 0.00 0 0.00
## q496 0.00 0 0.00
## q175 0.00 0 0.00
## q302 0.00 0 0.00
## q414 0.00 0 0.00
## age5 0.00 0 0.00
## q174 0.00 0 0.00
## q894 0.00 0 0.00
## age4 0.00 0 0.00
## grade4 0.00 0 0.00
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## qnothhpl2 23.34384
## q494 10.09149
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## q476 100
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## x3
## q873 82.23128
Orange County, FL (OL)
df_ol = df_fit_district %>%
filter(sitename == "Orange County, FL (OL)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ol,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
## Warning: from glmnet C++ code (error code -66); Convergence for 66th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -76); Convergence for 76th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -93); Convergence for 93th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -66); Convergence for 66th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -88); Convergence for 88th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -60); Convergence for 60th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
summary(model_lasso)
## Length Class Mode
## a0 177 -none- numeric
## beta 3 -none- list
## dfmat 177 -none- numeric
## df 59 -none- numeric
## dim 2 -none- numeric
## lambda 59 -none- numeric
## dev.ratio 59 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 27 1 0.03764936
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5660839 -0.010478878 0.14137336 0.27594528
## 2 1 1.149757e-03 0.5577506 -0.013928903 0.15312694 0.27810025
## 3 1 1.321941e-03 0.5654429 0.008994377 0.16229509 0.28696859
## 4 1 1.519911e-03 0.5577506 -0.029062055 0.14800219 0.25508458
## 5 1 1.747528e-03 0.5744172 -0.012562055 0.15014498 0.25352104
## 6 1 2.009233e-03 0.5744172 -0.028189962 0.15014498 0.25473481
## 7 1 2.310130e-03 0.5813520 -0.020585330 0.14263023 0.25181588
## 8 1 2.656088e-03 0.6071096 0.034244724 0.15559230 0.28873792
## 9 1 3.053856e-03 0.6154429 0.035176350 0.14620768 0.29080810
## 10 1 3.511192e-03 0.6245338 0.039497911 0.14418470 0.28505579
## 11 1 4.037017e-03 0.6336247 0.043528534 0.14783322 0.27012636
## 12 1 4.641589e-03 0.6245338 0.035099415 0.14418470 0.27762877
## 13 1 5.336699e-03 0.6328671 0.026707118 0.13279413 0.25689615
## 14 1 6.135907e-03 0.6495338 0.039217142 0.12446306 0.24516897
## 15 1 7.054802e-03 0.6669580 0.053930117 0.11074825 0.23361629
## 16 1 8.111308e-03 0.6663170 0.056674038 0.12611108 0.24027233
## 17 1 9.326033e-03 0.6983683 0.092889477 0.11009590 0.26069831
## 18 1 1.072267e-02 0.7074592 0.145571469 0.11632489 0.26209755
## 19 1 1.232847e-02 0.7234848 0.152457997 0.10502821 0.22961568
## 20 1 1.417474e-02 0.7234848 0.152457997 0.10502821 0.22961568
## 21 1 1.629751e-02 0.7241259 0.128071469 0.09530034 0.21321084
## 22 1 1.873817e-02 0.7331002 0.139725889 0.06053567 0.17696104
## 23 1 2.154435e-02 0.7407925 0.153734510 0.05965714 0.18645334
## 24 1 2.477076e-02 0.7407925 0.121176370 0.04489727 0.14667727
## 25 1 2.848036e-02 0.7498834 0.106949348 0.06637492 0.20559747
## 26 1 3.274549e-02 0.7498834 0.106949348 0.06637492 0.20559747
## 27 1 3.764936e-02 0.7582168 0.119159875 0.05965755 0.21247039
## 28 1 4.328761e-02 0.7414336 0.017361702 0.03981897 0.09815127
## 29 1 4.977024e-02 0.7497669 0.033547749 0.04938251 0.14731052
## 30 1 5.722368e-02 0.7574592 0.044186047 0.04525638 0.13972855
## 31 1 6.579332e-02 0.7574592 0.044186047 0.04525638 0.13972855
## 32 1 7.564633e-02 0.7491259 0.000000000 0.03657195 0.00000000
## 33 1 8.697490e-02 0.7491259 0.000000000 0.03657195 0.00000000
## 34 1 1.000000e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 35 1 1.149757e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 36 1 1.321941e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 37 1 1.519911e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 38 1 1.747528e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 39 1 2.009233e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 40 1 2.310130e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 41 1 2.656088e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 42 1 3.053856e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 43 1 3.511192e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 44 1 4.037017e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 45 1 4.641589e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 46 1 5.336699e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 47 1 6.135907e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 48 1 7.054802e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 49 1 8.111308e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 50 1 9.326033e-01 0.7491259 0.000000000 0.03657195 0.00000000
## 51 1 1.072267e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 52 1 1.232847e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 53 1 1.417474e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 54 1 1.629751e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 55 1 1.873817e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 56 1 2.154435e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 57 1 2.477076e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 58 1 2.848036e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 59 1 3.274549e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 60 1 3.764936e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 61 1 4.328761e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 62 1 4.977024e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 63 1 5.722368e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 64 1 6.579332e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 65 1 7.564633e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 66 1 8.697490e+00 0.7491259 0.000000000 0.03657195 0.00000000
## 67 1 1.000000e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 68 1 1.149757e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 69 1 1.321941e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 70 1 1.519911e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 71 1 1.747528e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 72 1 2.009233e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 73 1 2.310130e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 74 1 2.656088e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 75 1 3.053856e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 76 1 3.511192e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 77 1 4.037017e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 78 1 4.641589e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 79 1 5.336699e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 80 1 6.135907e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 81 1 7.054802e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 82 1 8.111308e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 83 1 9.326033e+01 0.7491259 0.000000000 0.03657195 0.00000000
## 84 1 1.072267e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 85 1 1.232847e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 86 1 1.417474e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 87 1 1.629751e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 88 1 1.873817e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 89 1 2.154435e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 90 1 2.477076e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 91 1 2.848036e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 92 1 3.274549e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 93 1 3.764936e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 94 1 4.328761e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 95 1 4.977024e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 96 1 5.722368e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 97 1 6.579332e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 98 1 7.564633e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 99 1 8.697490e+02 0.7491259 0.000000000 0.03657195 0.00000000
## 100 1 1.000000e+03 0.7491259 0.000000000 0.03657195 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 2.4 1.6 0.0
## 2 15.4 73.2 7.3
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.7561
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q896 0.000 0.0000 100.000
## q213 0.000 76.1176 0.000
## qnothhpl2 36.466 0.0000 0.000
## q474 0.000 35.6283 21.831
## q895 34.018 0.0000 0.000
## q654 25.246 0.0000 0.000
## q475 0.000 24.8464 0.000
## q633 22.425 0.0000 0.000
## q872 0.000 0.0000 16.352
## q496 0.000 16.3330 0.000
## age6 13.307 0.0000 0.000
## race43 0.000 11.2153 0.000
## q492 0.000 9.6719 0.000
## q494 8.223 0.0000 0.000
## grade3 0.000 6.3507 0.000
## q473 0.000 5.2787 0.000
## grade4 0.000 4.4407 0.000
## q173 0.000 3.4226 0.000
## q893 0.000 0.0000 1.037
## bmi 0.000 0.9664 0.000
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## qnothhpl2 36.46561
## q895 34.01832
## q654 25.24576
## q633 22.42473
## age6 13.30703
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## q213 76.11757
## q474 35.62827
## q475 24.84641
## q496 16.33300
## race43 11.21535
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## x3
## q896 100.00000
## q474 21.83091
## q872 16.35161
Palm Beach County, FL (PB)
df_ol = df_fit_district %>%
filter(sitename == "Palm Beach County, FL (PB)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ol,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 291 -none- numeric
## beta 3 -none- list
## dfmat 291 -none- numeric
## df 97 -none- numeric
## dim 2 -none- numeric
## lambda 97 -none- numeric
## dev.ratio 97 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 23 1 0.02154435
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.6781656 0.12466065 0.05315993 0.14478135
## 2 1 1.149757e-03 0.6781656 0.12466065 0.05315993 0.14478135
## 3 1 1.321941e-03 0.6849472 0.13248699 0.04484440 0.13515980
## 4 1 1.519911e-03 0.6915204 0.14520388 0.04214489 0.11956877
## 5 1 1.747528e-03 0.6947462 0.14954701 0.03934369 0.11774691
## 6 1 2.009233e-03 0.6978712 0.14914749 0.04257082 0.12771251
## 7 1 2.310130e-03 0.7009962 0.15136609 0.04766483 0.13169304
## 8 1 2.656088e-03 0.7011038 0.14216390 0.05169750 0.14570357
## 9 1 3.053856e-03 0.7011038 0.14216390 0.05169750 0.14570357
## 10 1 3.511192e-03 0.6979788 0.11538896 0.04147155 0.11254715
## 11 1 4.037017e-03 0.6981871 0.11537036 0.03931563 0.11443590
## 12 1 4.641589e-03 0.6983020 0.10728954 0.03841299 0.09328565
## 13 1 5.336699e-03 0.6985245 0.10199027 0.04798077 0.13129336
## 14 1 6.135907e-03 0.7018579 0.10995068 0.04666135 0.13402528
## 15 1 7.054802e-03 0.7083162 0.11936701 0.04936138 0.14249934
## 16 1 8.111308e-03 0.7181011 0.14441490 0.03855607 0.10829246
## 17 1 9.326033e-03 0.7179936 0.12334083 0.03959914 0.12807581
## 18 1 1.072267e-02 0.7216502 0.11977766 0.04069483 0.12653248
## 19 1 1.232847e-02 0.7317652 0.13784677 0.05374897 0.15281810
## 20 1 1.417474e-02 0.7348902 0.14232966 0.04962053 0.14958394
## 21 1 1.629751e-02 0.7481160 0.16295730 0.04239996 0.13641110
## 22 1 1.873817e-02 0.7477927 0.15017081 0.03797870 0.12120976
## 23 1 2.154435e-02 0.7511260 0.15629826 0.03803139 0.12366033
## 24 1 2.477076e-02 0.7477927 0.13832951 0.03797870 0.13261483
## 25 1 2.848036e-02 0.7413344 0.08101337 0.02783167 0.11211875
## 26 1 3.274549e-02 0.7446677 0.08648212 0.02407348 0.10592706
## 27 1 3.764936e-02 0.7413344 0.07062734 0.01675633 0.06974456
## 28 1 4.328761e-02 0.7381085 0.04556031 0.01950701 0.07830637
## 29 1 4.977024e-02 0.7380010 0.03287458 0.01429122 0.05779119
## 30 1 5.722368e-02 0.7348760 0.00625000 0.01478407 0.01976424
## 31 1 6.579332e-02 0.7348760 0.00000000 0.01478407 0.00000000
## 32 1 7.564633e-02 0.7348760 0.00000000 0.01478407 0.00000000
## 33 1 8.697490e-02 0.7348760 0.00000000 0.01478407 0.00000000
## 34 1 1.000000e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 35 1 1.149757e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 36 1 1.321941e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 37 1 1.519911e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 38 1 1.747528e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 39 1 2.009233e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 40 1 2.310130e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 41 1 2.656088e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 42 1 3.053856e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 43 1 3.511192e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 44 1 4.037017e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 45 1 4.641589e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 46 1 5.336699e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 47 1 6.135907e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 48 1 7.054802e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 49 1 8.111308e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 50 1 9.326033e-01 0.7348760 0.00000000 0.01478407 0.00000000
## 51 1 1.072267e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 52 1 1.232847e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 53 1 1.417474e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 54 1 1.629751e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 55 1 1.873817e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 56 1 2.154435e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 57 1 2.477076e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 58 1 2.848036e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 59 1 3.274549e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 60 1 3.764936e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 61 1 4.328761e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 62 1 4.977024e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 63 1 5.722368e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 64 1 6.579332e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 65 1 7.564633e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 66 1 8.697490e+00 0.7348760 0.00000000 0.01478407 0.00000000
## 67 1 1.000000e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 68 1 1.149757e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 69 1 1.321941e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 70 1 1.519911e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 71 1 1.747528e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 72 1 2.009233e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 73 1 2.310130e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 74 1 2.656088e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 75 1 3.053856e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 76 1 3.511192e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 77 1 4.037017e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 78 1 4.641589e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 79 1 5.336699e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 80 1 6.135907e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 81 1 7.054802e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 82 1 8.111308e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 83 1 9.326033e+01 0.7348760 0.00000000 0.01478407 0.00000000
## 84 1 1.072267e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 85 1 1.232847e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 86 1 1.417474e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 87 1 1.629751e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 88 1 1.873817e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 89 1 2.154435e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 90 1 2.477076e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 91 1 2.848036e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 92 1 3.274549e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 93 1 3.764936e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 94 1 4.328761e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 95 1 4.977024e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 96 1 5.722368e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 97 1 6.579332e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 98 1 7.564633e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 99 1 8.697490e+02 0.7348760 0.00000000 0.01478407 0.00000000
## 100 1 1.000000e+03 0.7348760 0.00000000 0.01478407 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 3.3 1.6 0.7
## 2 15.7 71.8 6.9
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.7508
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q214 0.00 100.000 0.00
## q495 92.19 0.000 0.00
## q215 62.39 0.000 0.00
## q177 61.01 0.000 0.00
## q176 0.00 57.224 30.71
## q496 11.95 36.090 0.00
## q633 0.00 22.219 0.00
## q894 0.00 20.076 0.00
## q174 0.00 19.497 0.00
## race42 18.99 0.000 0.00
## q872 15.66 0.000 18.93
## q413 18.17 0.000 0.00
## q897 0.00 17.552 0.00
## age4 17.30 0.000 0.00
## age6 13.43 0.000 0.00
## q532 0.00 12.415 0.00
## q475 12.24 0.000 0.00
## q472 0.00 8.569 11.51
## qnothhpl2 10.67 0.000 0.00
## q302 0.00 8.561 0.00
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## q495 92.19041
## q215 62.39230
## q177 61.01496
## race42 18.98873
## q413 18.16752
## age4 17.29543
## q872 15.65613
## age6 13.42826
## q475 12.24094
## q496 11.95278
## qnothhpl2 10.67079
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## q214 100.00000
## q176 57.22374
## q496 36.09019
## q633 22.21938
## q894 20.07588
## q174 19.49724
## q897 17.55170
## q532 12.41504
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## x3
## q176 30.71175
## q872 18.93355
## q472 11.51069
Pasco County, FL (PS)
df_ps = df_fit_district %>%
filter(sitename == "Pasco County, FL (PS)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ps,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 27 1 0.03764936
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.6744361 0.06305505 0.12480445 0.2826758
## 2 1 1.149757e-03 0.6694361 0.06016209 0.12687657 0.2852072
## 3 1 1.321941e-03 0.6694361 0.06016209 0.12687657 0.2852072
## 4 1 1.519911e-03 0.6644361 0.06715416 0.13084027 0.2798785
## 5 1 1.747528e-03 0.6696992 0.07012059 0.12562142 0.2800171
## 6 1 2.009233e-03 0.6744612 0.06743390 0.12438450 0.2739468
## 7 1 2.310130e-03 0.6744612 0.06743390 0.12438450 0.2739468
## 8 1 2.656088e-03 0.6794612 0.07223582 0.11974390 0.2686305
## 9 1 3.053856e-03 0.6847243 0.07799864 0.11593106 0.2697944
## 10 1 3.511192e-03 0.6897243 0.08426672 0.11772740 0.2715404
## 11 1 4.037017e-03 0.6894862 0.06795587 0.11119609 0.2373229
## 12 1 4.641589e-03 0.7047494 0.08351140 0.09293297 0.2282899
## 13 1 5.336699e-03 0.7150125 0.09621737 0.09678652 0.2449530
## 14 1 6.135907e-03 0.7202757 0.10540312 0.09949852 0.2539514
## 15 1 7.054802e-03 0.7202757 0.09538523 0.09949852 0.2594680
## 16 1 8.111308e-03 0.7400125 0.11516800 0.07677702 0.2450185
## 17 1 9.326033e-03 0.7500125 0.14790817 0.07749544 0.2533368
## 18 1 1.072267e-02 0.7550125 0.13908277 0.05405619 0.1974476
## 19 1 1.232847e-02 0.7650125 0.14244914 0.05195739 0.1889369
## 20 1 1.417474e-02 0.7700125 0.15055725 0.04719332 0.1797970
## 21 1 1.629751e-02 0.7750376 0.14280297 0.03420906 0.1316646
## 22 1 1.873817e-02 0.7650376 0.10787224 0.04659501 0.1308148
## 23 1 2.154435e-02 0.7600376 0.07994431 0.04508463 0.1194752
## 24 1 2.477076e-02 0.7600376 0.07994431 0.04508463 0.1194752
## 25 1 2.848036e-02 0.7700376 0.09591241 0.04121232 0.1388129
## 26 1 3.274549e-02 0.7803008 0.11620454 0.04020089 0.1570041
## 27 1 3.764936e-02 0.7853008 0.12609969 0.03910801 0.1644368
## 28 1 4.328761e-02 0.7803008 0.09817176 0.04020089 0.1591619
## 29 1 4.977024e-02 0.7803008 0.09817176 0.04020089 0.1591619
## 30 1 5.722368e-02 0.7803008 0.09817176 0.04020089 0.1591619
## 31 1 6.579332e-02 0.7750376 0.06368900 0.03420906 0.1353598
## 32 1 7.564633e-02 0.7652757 0.00000000 0.01990623 0.0000000
## 33 1 8.697490e-02 0.7652757 0.00000000 0.01990623 0.0000000
## 34 1 1.000000e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 35 1 1.149757e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 36 1 1.321941e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 37 1 1.519911e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 38 1 1.747528e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 39 1 2.009233e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 40 1 2.310130e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 41 1 2.656088e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 42 1 3.053856e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 43 1 3.511192e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 44 1 4.037017e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 45 1 4.641589e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 46 1 5.336699e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 47 1 6.135907e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 48 1 7.054802e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 49 1 8.111308e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 50 1 9.326033e-01 0.7652757 0.00000000 0.01990623 0.0000000
## 51 1 1.072267e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 52 1 1.232847e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 53 1 1.417474e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 54 1 1.629751e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 55 1 1.873817e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 56 1 2.154435e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 57 1 2.477076e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 58 1 2.848036e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 59 1 3.274549e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 60 1 3.764936e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 61 1 4.328761e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 62 1 4.977024e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 63 1 5.722368e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 64 1 6.579332e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 65 1 7.564633e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 66 1 8.697490e+00 0.7652757 0.00000000 0.01990623 0.0000000
## 67 1 1.000000e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 68 1 1.149757e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 69 1 1.321941e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 70 1 1.519911e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 71 1 1.747528e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 72 1 2.009233e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 73 1 2.310130e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 74 1 2.656088e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 75 1 3.053856e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 76 1 3.511192e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 77 1 4.037017e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 78 1 4.641589e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 79 1 5.336699e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 80 1 6.135907e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 81 1 7.054802e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 82 1 8.111308e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 83 1 9.326033e+01 0.7652757 0.00000000 0.01990623 0.0000000
## 84 1 1.072267e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 85 1 1.232847e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 86 1 1.417474e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 87 1 1.629751e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 88 1 1.873817e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 89 1 2.154435e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 90 1 2.477076e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 91 1 2.848036e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 92 1 3.274549e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 93 1 3.764936e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 94 1 4.328761e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 95 1 4.977024e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 96 1 5.722368e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 97 1 6.579332e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 98 1 7.564633e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 99 1 8.697490e+02 0.7652757 0.00000000 0.01990623 0.0000000
## 100 1 1.000000e+03 0.7652757 0.00000000 0.01990623 0.0000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 2.0 0.0 0.0
## 2 17.0 76.5 4.5
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.785
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q178 100.0000 0.0000 0.00
## q475 0.0000 0.0000 74.58
## q493 17.7502 0.0000 0.00
## sex2 17.3787 0.0000 0.00
## qnothhpl2 0.0000 14.8690 0.00
## q502 0.0000 14.0383 0.00
## grade4 12.5549 0.0000 0.00
## q302 10.6132 0.0000 0.00
## q492 7.2529 0.0000 0.00
## q653 0.0000 3.5920 0.00
## q873 0.0000 3.4580 0.00
## q232 2.0557 0.0000 0.00
## q214 0.5358 0.0000 0.00
## q173 0.0000 0.4988 0.00
## q654 0.2829 0.0000 0.00
## q897 0.0000 0.1906 0.00
## q494 0.0000 0.0000 0.00
## bmi 0.0000 0.0000 0.00
## q172 0.0000 0.0000 0.00
## age7 0.0000 0.0000 0.00
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## q178 100.00000
## q493 17.75018
## sex2 17.37868
## grade4 12.55491
## q302 10.61321
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## qnothhpl2 14.86900
## q502 14.03834
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## x3
## q475 74.57636
Philadelphia, PA (PH)
df_ph = df_fit_district %>%
filter(sitename == "Philadelphia, PA (PH)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_ph,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 100 1 1000
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5186683 0.07733753 0.09309386 0.17854681
## 2 1 1.149757e-03 0.5186683 0.08056257 0.09309386 0.17650232
## 3 1 1.321941e-03 0.5186683 0.08056257 0.09309386 0.17650232
## 4 1 1.519911e-03 0.5186683 0.07505331 0.09309386 0.17411897
## 5 1 1.747528e-03 0.5186683 0.07505331 0.09309386 0.17411897
## 6 1 2.009233e-03 0.5186683 0.07505331 0.09309386 0.17411897
## 7 1 2.310130e-03 0.4947712 0.03718210 0.09267842 0.17139319
## 8 1 2.656088e-03 0.4947712 0.03316585 0.09267842 0.17662240
## 9 1 3.053856e-03 0.4947712 0.03316585 0.09267842 0.17662240
## 10 1 3.511192e-03 0.5065359 0.05741450 0.10430248 0.19218622
## 11 1 4.037017e-03 0.5183007 0.07410698 0.10996461 0.20560436
## 12 1 4.641589e-03 0.5352941 0.09477713 0.09454936 0.18662554
## 13 1 5.336699e-03 0.5408497 0.08651779 0.07165156 0.14526889
## 14 1 6.135907e-03 0.5408497 0.08651779 0.07165156 0.14526889
## 15 1 7.054802e-03 0.5467320 0.10034682 0.07809857 0.15973214
## 16 1 8.111308e-03 0.5460376 0.09870028 0.08385103 0.15615189
## 17 1 9.326033e-03 0.5460376 0.09564373 0.08385103 0.15692277
## 18 1 1.072267e-02 0.5571078 0.11336756 0.10926043 0.19690213
## 19 1 1.232847e-02 0.5515523 0.09662444 0.11453230 0.21440645
## 20 1 1.417474e-02 0.5688725 0.12072345 0.10914199 0.20583180
## 21 1 1.629751e-02 0.5865196 0.14880652 0.10569709 0.19714452
## 22 1 1.873817e-02 0.5865196 0.14950821 0.11936337 0.22498409
## 23 1 2.154435e-02 0.5812908 0.13199388 0.12022318 0.22947303
## 24 1 2.477076e-02 0.5875408 0.12946782 0.09435755 0.18888244
## 25 1 2.848036e-02 0.5882353 0.11582140 0.10802454 0.24372226
## 26 1 3.274549e-02 0.5937908 0.11368286 0.08932528 0.19955050
## 27 1 3.764936e-02 0.5931373 0.08691107 0.07399794 0.15166371
## 28 1 4.328761e-02 0.5800245 0.02504156 0.07833592 0.15307247
## 29 1 4.977024e-02 0.5623366 -0.03953847 0.07546674 0.11730498
## 30 1 5.722368e-02 0.5748366 -0.03337391 0.03584689 0.05822237
## 31 1 6.579332e-02 0.5928922 -0.01056911 0.02617228 0.03342245
## 32 1 7.564633e-02 0.5987745 0.00000000 0.01418451 0.00000000
## 33 1 8.697490e-02 0.5987745 0.00000000 0.01418451 0.00000000
## 34 1 1.000000e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 35 1 1.149757e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 36 1 1.321941e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 37 1 1.519911e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 38 1 1.747528e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 39 1 2.009233e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 40 1 2.310130e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 41 1 2.656088e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 42 1 3.053856e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 43 1 3.511192e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 44 1 4.037017e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 45 1 4.641589e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 46 1 5.336699e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 47 1 6.135907e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 48 1 7.054802e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 49 1 8.111308e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 50 1 9.326033e-01 0.5987745 0.00000000 0.01418451 0.00000000
## 51 1 1.072267e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 52 1 1.232847e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 53 1 1.417474e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 54 1 1.629751e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 55 1 1.873817e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 56 1 2.154435e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 57 1 2.477076e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 58 1 2.848036e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 59 1 3.274549e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 60 1 3.764936e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 61 1 4.328761e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 62 1 4.977024e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 63 1 5.722368e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 64 1 6.579332e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 65 1 7.564633e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 66 1 8.697490e+00 0.5987745 0.00000000 0.01418451 0.00000000
## 67 1 1.000000e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 68 1 1.149757e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 69 1 1.321941e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 70 1 1.519911e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 71 1 1.747528e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 72 1 2.009233e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 73 1 2.310130e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 74 1 2.656088e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 75 1 3.053856e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 76 1 3.511192e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 77 1 4.037017e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 78 1 4.641589e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 79 1 5.336699e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 80 1 6.135907e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 81 1 7.054802e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 82 1 8.111308e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 83 1 9.326033e+01 0.5987745 0.00000000 0.01418451 0.00000000
## 84 1 1.072267e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 85 1 1.232847e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 86 1 1.417474e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 87 1 1.629751e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 88 1 1.873817e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 89 1 2.154435e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 90 1 2.477076e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 91 1 2.848036e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 92 1 3.274549e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 93 1 3.764936e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 94 1 4.328761e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 95 1 4.977024e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 96 1 5.722368e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 97 1 6.579332e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 98 1 7.564633e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 99 1 8.697490e+02 0.5987745 0.00000000 0.01418451 0.00000000
## 100 1 1.000000e+03 0.5987745 0.00000000 0.01418451 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 59.9 34.3 5.8
## 2 0.0 0.0 0.0
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.5988
varImp(model_lasso, scale = FALSE)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q414 0 0 0
## race43 0 0 0
## race44 0 0 0
## age7 0 0 0
## q475 0 0 0
## sex2 0 0 0
## q492 0 0 0
## q895 0 0 0
## q493 0 0 0
## q174 0 0 0
## q262 0 0 0
## q413 0 0 0
## age5 0 0 0
## q173 0 0 0
## q872 0 0 0
## age4 0 0 0
## grade4 0 0 0
## q496 0 0 0
## q896 0 0 0
## q495 0 0 0
plot(varImp(model_lasso))
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## [1] x1
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## [1] x2
## <0 rows> (or 0-length row.names)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## [1] x3
## <0 rows> (or 0-length row.names)
Portland, OR (PO)
df_po = df_fit_district %>%
filter(sitename == "Portland, OR (PO)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_po,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 32 1 0.07564633
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5119481 0.049588805 0.07799131 0.13037224
## 2 1 1.149757e-03 0.5119481 0.049588805 0.07799131 0.13037224
## 3 1 1.321941e-03 0.5119481 0.049588805 0.07799131 0.13037224
## 4 1 1.519911e-03 0.5119481 0.049588805 0.07799131 0.13037224
## 5 1 1.747528e-03 0.5167100 0.057319068 0.08021795 0.13453187
## 6 1 2.009233e-03 0.5260173 0.069394380 0.07252298 0.12912243
## 7 1 2.310130e-03 0.5307792 0.071354720 0.07042067 0.12754127
## 8 1 2.656088e-03 0.5353247 0.075704530 0.06967626 0.13010073
## 9 1 3.053856e-03 0.5491775 0.096759630 0.07677614 0.14824267
## 10 1 3.511192e-03 0.5489394 0.098789365 0.07807054 0.16471786
## 11 1 4.037017e-03 0.5580303 0.113719505 0.08277110 0.17139176
## 12 1 4.641589e-03 0.5720996 0.132875108 0.09696618 0.19134619
## 13 1 5.336699e-03 0.5673377 0.125517750 0.09556355 0.18892822
## 14 1 6.135907e-03 0.5584632 0.096351228 0.09163119 0.16738034
## 15 1 7.054802e-03 0.5679870 0.117661308 0.09786574 0.18411159
## 16 1 8.111308e-03 0.5632251 0.106210383 0.09625038 0.17749912
## 17 1 9.326033e-03 0.5632251 0.106210383 0.09625038 0.17749912
## 18 1 1.072267e-02 0.5677706 0.107155586 0.09874403 0.19076721
## 19 1 1.232847e-02 0.5907143 0.144470629 0.09709783 0.18945419
## 20 1 1.417474e-02 0.5952597 0.153765524 0.09337181 0.18211554
## 21 1 1.629751e-02 0.5909307 0.144398050 0.09100126 0.17316109
## 22 1 1.873817e-02 0.5909307 0.144398050 0.09100126 0.17316109
## 23 1 2.154435e-02 0.5907143 0.138862828 0.08086323 0.15511653
## 24 1 2.477076e-02 0.6000216 0.150803791 0.08474169 0.16352387
## 25 1 2.848036e-02 0.5959091 0.136925087 0.08153868 0.15743209
## 26 1 3.274549e-02 0.5961255 0.133543740 0.08854236 0.16920907
## 27 1 3.764936e-02 0.5866234 0.094281657 0.06543195 0.12224392
## 28 1 4.328761e-02 0.5723377 0.047652162 0.07275173 0.16003689
## 29 1 4.977024e-02 0.6002597 0.081768537 0.07652676 0.17614662
## 30 1 5.722368e-02 0.5911688 0.042013821 0.05104074 0.12971087
## 31 1 6.579332e-02 0.6050216 0.049335423 0.03829760 0.09470555
## 32 1 7.564633e-02 0.6102165 0.036275665 0.02117056 0.06672942
## 33 1 8.697490e-02 0.6011255 0.005617978 0.01694922 0.01776560
## 34 1 1.000000e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 35 1 1.149757e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 36 1 1.321941e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 37 1 1.519911e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 38 1 1.747528e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 39 1 2.009233e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 40 1 2.310130e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 41 1 2.656088e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 42 1 3.053856e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 43 1 3.511192e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 44 1 4.037017e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 45 1 4.641589e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 46 1 5.336699e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 47 1 6.135907e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 48 1 7.054802e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 49 1 8.111308e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 50 1 9.326033e-01 0.6011255 0.000000000 0.01694922 0.00000000
## 51 1 1.072267e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 52 1 1.232847e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 53 1 1.417474e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 54 1 1.629751e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 55 1 1.873817e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 56 1 2.154435e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 57 1 2.477076e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 58 1 2.848036e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 59 1 3.274549e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 60 1 3.764936e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 61 1 4.328761e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 62 1 4.977024e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 63 1 5.722368e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 64 1 6.579332e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 65 1 7.564633e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 66 1 8.697490e+00 0.6011255 0.000000000 0.01694922 0.00000000
## 67 1 1.000000e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 68 1 1.149757e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 69 1 1.321941e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 70 1 1.519911e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 71 1 1.747528e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 72 1 2.009233e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 73 1 2.310130e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 74 1 2.656088e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 75 1 3.053856e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 76 1 3.511192e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 77 1 4.037017e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 78 1 4.641589e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 79 1 5.336699e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 80 1 6.135907e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 81 1 7.054802e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 82 1 8.111308e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 83 1 9.326033e+01 0.6011255 0.000000000 0.01694922 0.00000000
## 84 1 1.072267e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 85 1 1.232847e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 86 1 1.417474e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 87 1 1.629751e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 88 1 1.873817e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 89 1 2.154435e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 90 1 2.477076e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 91 1 2.848036e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 92 1 3.274549e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 93 1 3.764936e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 94 1 4.328761e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 95 1 4.977024e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 96 1 5.722368e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 97 1 6.579332e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 98 1 7.564633e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 99 1 8.697490e+02 0.6011255 0.000000000 0.01694922 0.00000000
## 100 1 1.000000e+03 0.6011255 0.000000000 0.01694922 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 1.9 0.9 0.0
## 2 33.8 59.2 4.2
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.6103
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## age7 0.00 100.00 0
## grade2 0.00 95.15 0
## qnothhpl2 90.60 0.00 0
## sex2 75.05 0.00 0
## q472 0.00 0.00 0
## q172 0.00 0.00 0
## q173 0.00 0.00 0
## grade3 0.00 0.00 0
## q494 0.00 0.00 0
## grade4 0.00 0.00 0
## q496 0.00 0.00 0
## q502 0.00 0.00 0
## q178 0.00 0.00 0
## q414 0.00 0.00 0
## q417 0.00 0.00 0
## age5 0.00 0.00 0
## q177 0.00 0.00 0
## q894 0.00 0.00 0
## age4 0.00 0.00 0
## race44 0.00 0.00 0
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## qnothhpl2 90.60028
## sex2 75.04838
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## age7 100.00000
## grade2 95.15292
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## [1] x3
## <0 rows> (or 0-length row.names)
San Francisco, CA (SF)
df_sf = df_fit_district %>%
filter(sitename == "San Francisco, CA (SF)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_sf,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
summary(model_lasso)
## Length Class Mode
## a0 297 -none- numeric
## beta 3 -none- list
## dfmat 297 -none- numeric
## df 99 -none- numeric
## dim 2 -none- numeric
## lambda 99 -none- numeric
## dev.ratio 99 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 27 1 0.03764936
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.4385338 -0.0305150648 0.08639463 0.15493463
## 2 1 1.149757e-03 0.4540602 -0.0056809037 0.08870194 0.14188284
## 3 1 1.321941e-03 0.4588221 -0.0006961685 0.09126322 0.14793956
## 4 1 1.519911e-03 0.4635840 0.0008243599 0.08501653 0.15015252
## 5 1 1.747528e-03 0.4781078 0.0113951840 0.08594813 0.15930363
## 6 1 2.009233e-03 0.4781078 0.0149564375 0.08594813 0.16037042
## 7 1 2.310130e-03 0.4781078 0.0149564375 0.08594813 0.16037042
## 8 1 2.656088e-03 0.4783459 0.0080587209 0.08134444 0.14629250
## 9 1 3.053856e-03 0.4835840 0.0154844107 0.09982596 0.18788852
## 10 1 3.511192e-03 0.4835840 0.0154844107 0.09982596 0.18788852
## 11 1 4.037017e-03 0.4838221 0.0108332740 0.09265504 0.18436936
## 12 1 4.641589e-03 0.4790602 0.0048785349 0.09693491 0.18344954
## 13 1 5.336699e-03 0.4890602 0.0101974854 0.08170381 0.14836123
## 14 1 6.135907e-03 0.4995614 0.0245329334 0.09452813 0.16113154
## 15 1 7.054802e-03 0.4940602 0.0128641126 0.08629989 0.13526090
## 16 1 8.111308e-03 0.4992982 0.0210334763 0.10827821 0.17293564
## 17 1 9.326033e-03 0.5140602 0.0457386506 0.10295899 0.17227567
## 18 1 1.072267e-02 0.5145363 0.0403617586 0.13188800 0.22892790
## 19 1 1.232847e-02 0.5097744 0.0258727868 0.14157188 0.24015763
## 20 1 1.417474e-02 0.5092982 0.0226464899 0.11287056 0.17333188
## 21 1 1.629751e-02 0.5390852 0.0674873815 0.11748087 0.18143199
## 22 1 1.873817e-02 0.5446115 0.0718968313 0.11407382 0.18569243
## 23 1 2.154435e-02 0.5638972 0.1013504551 0.10733584 0.16967040
## 24 1 2.477076e-02 0.5588972 0.0889134109 0.11102252 0.18418936
## 25 1 2.848036e-02 0.5834461 0.1268433250 0.11088081 0.18815243
## 26 1 3.274549e-02 0.5829449 0.1170980351 0.10237424 0.17088851
## 27 1 3.764936e-02 0.5979449 0.1363629518 0.09832778 0.16983032
## 28 1 4.328761e-02 0.5791353 0.0967020848 0.12374845 0.21514623
## 29 1 4.977024e-02 0.5736341 0.0719850006 0.10167298 0.16162266
## 30 1 5.722368e-02 0.5783960 0.0651560273 0.09233196 0.15591646
## 31 1 6.579332e-02 0.5771805 0.0357077911 0.04212045 0.07640476
## 32 1 7.564633e-02 0.5719424 0.0138045661 0.03039285 0.05965034
## 33 1 8.697490e-02 0.5717043 0.0000000000 0.02514556 0.00000000
## 34 1 1.000000e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 35 1 1.149757e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 36 1 1.321941e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 37 1 1.519911e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 38 1 1.747528e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 39 1 2.009233e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 40 1 2.310130e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 41 1 2.656088e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 42 1 3.053856e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 43 1 3.511192e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 44 1 4.037017e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 45 1 4.641589e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 46 1 5.336699e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 47 1 6.135907e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 48 1 7.054802e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 49 1 8.111308e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 50 1 9.326033e-01 0.5717043 0.0000000000 0.02514556 0.00000000
## 51 1 1.072267e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 52 1 1.232847e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 53 1 1.417474e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 54 1 1.629751e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 55 1 1.873817e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 56 1 2.154435e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 57 1 2.477076e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 58 1 2.848036e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 59 1 3.274549e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 60 1 3.764936e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 61 1 4.328761e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 62 1 4.977024e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 63 1 5.722368e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 64 1 6.579332e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 65 1 7.564633e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 66 1 8.697490e+00 0.5717043 0.0000000000 0.02514556 0.00000000
## 67 1 1.000000e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 68 1 1.149757e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 69 1 1.321941e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 70 1 1.519911e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 71 1 1.747528e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 72 1 2.009233e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 73 1 2.310130e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 74 1 2.656088e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 75 1 3.053856e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 76 1 3.511192e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 77 1 4.037017e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 78 1 4.641589e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 79 1 5.336699e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 80 1 6.135907e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 81 1 7.054802e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 82 1 8.111308e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 83 1 9.326033e+01 0.5717043 0.0000000000 0.02514556 0.00000000
## 84 1 1.072267e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 85 1 1.232847e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 86 1 1.417474e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 87 1 1.629751e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 88 1 1.873817e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 89 1 2.154435e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 90 1 2.477076e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 91 1 2.848036e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 92 1 3.274549e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 93 1 3.764936e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 94 1 4.328761e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 95 1 4.977024e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 96 1 5.722368e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 97 1 6.579332e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 98 1 7.564633e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 99 1 8.697490e+02 0.5717043 0.0000000000 0.02514556 0.00000000
## 100 1 1.000000e+03 0.5717043 0.0000000000 0.02514556 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 8.4 5.9 2.0
## 2 27.6 51.2 4.9
## 3 0.0 0.0 0.0
##
## Accuracy (average) : 0.5961
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q492 0.6318 0.000 100
## q178 92.3574 0.000 0
## q415 85.0255 0.000 0
## qnothhpl2 0.0000 70.656 0
## q302 0.0000 44.693 0
## q474 42.3052 0.000 0
## age4 15.7485 42.257 0
## q175 40.1721 0.000 0
## q495 0.0000 33.948 0
## q653 0.0000 21.010 0
## q212 0.0000 19.264 0
## age6 15.9270 0.000 0
## q873 12.7954 0.000 0
## race43 0.0000 9.292 0
## q472 0.0000 7.647 0
## q476 7.5538 0.000 0
## q654 0.0000 6.147 0
## q494 1.6113 0.000 0
## q522 0.0000 0.000 0
## q172 0.0000 0.000 0
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## q178 92.35737
## q415 85.02549
## q474 42.30519
## q175 40.17212
## age6 15.92697
## age4 15.74855
## q873 12.79543
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## qnothhpl2 70.65601
## q302 44.69343
## age4 42.25721
## q495 33.94785
## q653 21.01046
## q212 19.26352
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## x3
## q492 100
Shelby County, TN (ST)
df_sf = df_fit_district %>%
filter(sitename == "Shelby County, TN (ST)") %>%
select(-sitename, -year)
set.seed(123)
#Create grid to search lambda
lambda <- 10^seq(-3,3, length = 100)
# Specify training control
train_control_lasso <- trainControl(method = "cv", number = 10)
model_lasso = train(q85 ~.,
df_sf,
method = "glmnet",
trControl = train_control_lasso,
tuneGrid = expand.grid(alpha = 1, lambda = lambda))
## Warning: from glmnet C++ code (error code -92); Convergence for 92th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -75); Convergence for 75th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -75); Convergence for 75th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -80); Convergence for 80th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -77); Convergence for 77th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet C++ code (error code -74); Convergence for 74th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
summary(model_lasso)
## Length Class Mode
## a0 300 -none- numeric
## beta 3 -none- list
## dfmat 300 -none- numeric
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 3 -none- character
## grouped 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 58 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 3 -none- character
## param 0 -none- list
model_lasso$bestTune
## alpha lambda
## 29 1 0.04977024
model_lasso$results
## alpha lambda Accuracy Kappa AccuracySD KappaSD
## 1 1 1.000000e-03 0.5359804 0.064087964 0.10053693 0.22262612
## 2 1 1.149757e-03 0.5426471 0.077180382 0.08757591 0.20483333
## 3 1 1.321941e-03 0.5488971 0.085114718 0.09130115 0.21056330
## 4 1 1.519911e-03 0.5488971 0.076460694 0.09130115 0.20989016
## 5 1 1.747528e-03 0.5496814 0.085902774 0.09611268 0.21649502
## 6 1 2.009233e-03 0.5437990 0.068546978 0.10304157 0.20660795
## 7 1 2.310130e-03 0.5496814 0.076175183 0.11096570 0.21391089
## 8 1 2.656088e-03 0.5496814 0.076175183 0.11096570 0.21391089
## 9 1 3.053856e-03 0.5430637 0.059014393 0.11057306 0.22177050
## 10 1 3.511192e-03 0.5493137 0.051422068 0.10140418 0.23342934
## 11 1 4.037017e-03 0.5555637 0.052892305 0.10828034 0.24127840
## 12 1 4.641589e-03 0.5618137 0.075068327 0.10650605 0.22170740
## 13 1 5.336699e-03 0.5743627 0.090828929 0.10686482 0.23354335
## 14 1 6.135907e-03 0.5935294 0.111820618 0.08856979 0.20906639
## 15 1 7.054802e-03 0.5927451 0.102337102 0.09543254 0.21084300
## 16 1 8.111308e-03 0.5994118 0.098225983 0.07789399 0.17812669
## 17 1 9.326033e-03 0.5994118 0.098225983 0.07789399 0.17812669
## 18 1 1.072267e-02 0.6174265 0.120351794 0.06684309 0.17940402
## 19 1 1.232847e-02 0.6424265 0.157418338 0.06476346 0.16423768
## 20 1 1.417474e-02 0.6490931 0.165007624 0.06332622 0.16524046
## 21 1 1.629751e-02 0.6490931 0.165007624 0.06332622 0.16524046
## 22 1 1.873817e-02 0.6553431 0.166659698 0.07617296 0.19072203
## 23 1 2.154435e-02 0.6427941 0.138685686 0.07678515 0.17720258
## 24 1 2.477076e-02 0.6432108 0.125111341 0.07948066 0.18763904
## 25 1 2.848036e-02 0.6494608 0.121087230 0.07474502 0.19267967
## 26 1 3.274549e-02 0.6557108 0.118624848 0.07508468 0.17008730
## 27 1 3.764936e-02 0.6560784 0.098331337 0.07837483 0.16724577
## 28 1 4.328761e-02 0.6623284 0.077952516 0.05127740 0.12611030
## 29 1 4.977024e-02 0.6694118 0.036128529 0.03390568 0.11385368
## 30 1 5.722368e-02 0.6627451 0.003780585 0.02543725 0.08043915
## 31 1 6.579332e-02 0.6564951 -0.017011494 0.02634123 0.03689637
## 32 1 7.564633e-02 0.6627451 -0.010344828 0.02543725 0.03271322
## 33 1 8.697490e-02 0.6689951 0.000000000 0.02265924 0.00000000
## 34 1 1.000000e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 35 1 1.149757e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 36 1 1.321941e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 37 1 1.519911e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 38 1 1.747528e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 39 1 2.009233e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 40 1 2.310130e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 41 1 2.656088e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 42 1 3.053856e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 43 1 3.511192e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 44 1 4.037017e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 45 1 4.641589e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 46 1 5.336699e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 47 1 6.135907e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 48 1 7.054802e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 49 1 8.111308e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 50 1 9.326033e-01 0.6689951 0.000000000 0.02265924 0.00000000
## 51 1 1.072267e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 52 1 1.232847e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 53 1 1.417474e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 54 1 1.629751e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 55 1 1.873817e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 56 1 2.154435e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 57 1 2.477076e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 58 1 2.848036e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 59 1 3.274549e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 60 1 3.764936e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 61 1 4.328761e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 62 1 4.977024e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 63 1 5.722368e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 64 1 6.579332e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 65 1 7.564633e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 66 1 8.697490e+00 0.6689951 0.000000000 0.02265924 0.00000000
## 67 1 1.000000e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 68 1 1.149757e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 69 1 1.321941e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 70 1 1.519911e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 71 1 1.747528e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 72 1 2.009233e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 73 1 2.310130e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 74 1 2.656088e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 75 1 3.053856e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 76 1 3.511192e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 77 1 4.037017e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 78 1 4.641589e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 79 1 5.336699e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 80 1 6.135907e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 81 1 7.054802e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 82 1 8.111308e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 83 1 9.326033e+01 0.6689951 0.000000000 0.02265924 0.00000000
## 84 1 1.072267e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 85 1 1.232847e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 86 1 1.417474e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 87 1 1.629751e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 88 1 1.873817e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 89 1 2.154435e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 90 1 2.477076e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 91 1 2.848036e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 92 1 3.274549e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 93 1 3.764936e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 94 1 4.328761e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 95 1 4.977024e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 96 1 5.722368e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 97 1 6.579332e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 98 1 7.564633e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 99 1 8.697490e+02 0.6689951 0.000000000 0.02265924 0.00000000
## 100 1 1.000000e+03 0.6689951 0.000000000 0.02265924 0.00000000
#Visualize accuracy versus values of C
plot(model_lasso)

#Obtain metrics of accuracy from training
confusionMatrix(model_lasso)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3
## 1 1.2 0.6 0.6
## 2 25.0 65.6 6.2
## 3 0.0 0.6 0.0
##
## Accuracy (average) : 0.6687
varImp(model_lasso)
## glmnet variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 58)
##
## 1 2 3
## q216 0.000 0.000 100.00
## q176 86.440 0.000 0.00
## q414 78.603 0.000 0.00
## q472 0.000 41.690 0.00
## q178 0.000 4.219 41.63
## q897 0.000 25.178 0.00
## race42 0.000 23.406 0.00
## sex2 18.158 0.000 0.00
## q473 15.500 13.745 0.00
## grade4 0.000 6.549 0.00
## q172 4.106 0.000 0.00
## q412 0.000 3.371 0.00
## q476 0.000 2.860 0.00
## qnothhpl2 2.461 0.000 0.00
## q475 2.031 0.000 0.00
## q502 0.000 0.000 0.00
## q173 0.000 0.000 0.00
## q174 0.000 0.000 0.00
## age7 0.000 0.000 0.00
## q873 0.000 0.000 0.00
plot(varImp(model_lasso))

var_importance = varImp(model_lasso)
var_importance$importance %>%
janitor::clean_names() %>%
filter(x1 > 10) %>%
select(x1) %>%
arrange(desc(x1))
## x1
## q176 86.44038
## q414 78.60256
## sex2 18.15770
## q473 15.50019
var_importance$importance %>%
janitor::clean_names() %>%
filter(x2 > 10) %>%
select(x2) %>%
arrange(desc(x2))
## x2
## q472 41.68957
## q897 25.17849
## race42 23.40621
## q473 13.74520
var_importance$importance %>%
janitor::clean_names() %>%
filter(x3 > 10) %>%
select(x3) %>%
arrange(desc(x3))
## x3
## q216 100.00000
## q178 41.63071